The industry is not there yet, but getting it right will almost certainly boost sales.

So, picture the scene: a customer buys a flight to London in October. Ask yourself these questions:

Does she need an hotel for this stay, and if so what type of hotel?

Is the trip for business or personal reasons?

What are the Google keywords she used to reach the site: “cheap weekend”, “romantic weekend”, “airport hotel with swimming pool”?

How will she get to the airport – by cab or will she need parking?

At the destination, how will she get to the hotel – by train or taxi?

If the trip is for pleasure what other services or activities might she need or want?

Does she know about plays, concerts or art exhibitions in town?

What type of tickets did she purchase from us in the past?

Looking at her public profile, what activities could you offer?

And the list of questions goes on.

Based on history data and some intelligent predictive analytics, firms can now get reasonably close to guessing what products this customer may want.

So we identified some common myths in what many see as a complicated area.

Myth 1: Big Data is a new phenomenon

No, says, Dario Cardile, chief innovation marketing at Bravofly, in fact Big Data has been used by global telephone and mobile companies for almost 20 years.

One notable example, he says, is Teradata which has provided a technical solution for large data or analytics sets for a couple of decades. However, the expansion of many global ecommerce businesses, as well as the technology available today has made it more accessible.

And the recession has, in fact, pushed many organisations to leverage their Big Data to get more bang from their infrastructure and data sets.

What is new is that the need to consolidate different and disparate data sources from for example the site, operations, mobile, social and so on.

Myth 2: You need tonnes of money to deliver with Big Data

You don’t, says Cardile, but “you do need damn good people”. And herein lies the challenge. Finding people that really understand analytics is anything but easy.

They need to do more than simply produce tonnes of reports and then pivot. People need to be highly skilled and able to go behind the numbers, read the trends, spot the outliers and make the connections

“I come across thousands of smart ‘business intelligence’ resources, but have hired very few ‘analytical’ ones,” Cardile says.

According to Freddy Bodin, revenue management system director at Accor, the cost of analysing data has come down.

“What was costly yesterday in terms of data treatment is now a piece of cake,” he says, adding that revenue management data capture used to take three hours of treatment on a standalone PC, but today takes less than ten minutes on a central platform.

And this can produce a similar, or even better, forecast.

Myth 3: Big data is here today, gone tomorrow

Rapid technical developments mean that just because something isn’t used today, doesn’t mean it cannot be used at a later date, says Bodin.

“Yesterday, we needed to aggregate data first, and then model it. So if you want to change your angle of analysis, you have to wait until you have gathered enough data,” he adds.

For Bodin, Big Data can deliver tremendous information if:

You have the correct data-mining tools to make the data talk

If you have the time to analyse the data – because how this is done today is totally new. There is a need to automate more and to tackle data quality with alerts.

Myth 4: Fraud is overrated

The issue of compliance and privacy is an incredibly important one and one that Cardile takes very seriously.

“The Edward Snowden scandal opened the eyes for many people. As leaders we should always put the customer at heart of everything and put both the legal and moral considerations around data usage above all commercial and marketing objectives,” he says.

With regards to security, this should never be an extra, but part of company’s DNA.

In Germany, for example, there are high data security regulations but in other countries less so. However, Best Western understands only too well that there is a need to ensure regulations are met at any point.

Myth 5: One-size fits all

Each business has to identify how they can leverage on their data to build better services, expand their product lines and ultimately keep customers sweet.

“There is no in-my-humble-opinion a best practice,” says Cardile.

It all depends on who the customer is and today that customer is very sophisticated; they expect travel brands to give them what they are asking for, and where they are asking for it.

“In the past they often consumed what marketers presented them,” explains Bachelin, but today they expect the right and correct information starting with their initial search, but also throughout their stay.

In other words, you need to meet the customer where you find them.

Myth 6: Big data is the answer to all forecasting challenges

When it comes to forecasting precisely, firms need more than just Big Data.

“A mathematical algorithm needs to have enough occurrences to be stable,” explains Bodin. “This means that we are not working on individual types of data, but by segment type. We need to aggregate data to compose good statistics.”

That said, big data can help with RM forecasting by providing additional information – in others words working with data that isn’t always your own.

“In the case of a hotel booking, one could, for example, use weather forecasts, economic trends, competitor prices, or airline bookings in a destination… even RSS feeds, can be used in real time,” he says.

One could even take into account what is happening right now, in the place a hotel is situated – for example, is there an imminent train strike?

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Gadi Bashvitz

Very interesting article, thanks for posting. As a Big Data company enabling frequent travelers to have an automated hotel booking experience we know this field VERY well. I would add that focus is a critical factor in making Big Data targeting work well. Check out http://www.olset.com to see how you can book a hotel in a seamless way relying on Big Data.